The present invention is directed to a classifier cascade that can be used to detect objects within a scene. While existing classifier cascades exist, such existing systems do not employ a diverse and complimentary set of features for object detection.
For example, H. Schneiderman proposes a feature-level optimization of a cascade object detector, taking advantage of the redundancies in computing features for various analysis windows (see Literature Reference No. 3). Schneiderman's approach is tuned for an exhaustive search of an image where neighboring analysis windows have a large common area. The efficiency gain is reduced where the search pattern is sparse, such as is the case when using particle swarm optimization.
Alternatively, other methods collect bootstrapped negative samples for training. Existing approaches exhaustively search for a collection of “hard” samples (see Literature Reference Nos. 1-3). Such a search performed using features and classifiers other than Haar-like features from integral images and ensemble classifiers, features and classifiers that are compute-intensive become problematic (see Literature Reference Nos. 1-4). Features like histogram-of-oriented gradients, edge-symmetry features, and classifiers like kernel support-vector-machines make collecting training samples by exhaustive search not feasible.
Finally, existing approaches to the classifier-cascade do not use a heterogeneous set of features and classifiers in the construction of the cascade.
Thus, a continuing need exists for an object detection system that is efficient in finding objects, that is employed to rapidly find the “hard” set of negative training samples (despite the computationally intense features and classifiers), and that allows for fine tuning of the appropriate set of features and classifiers for use with specific object types.